fix "\\n" issue: replace "\\n" with "\n"

FlagEmbedding/abc/evaluation/arguments.py

@@ -177,3 +177,14 @@ class AbsEvalModelArgs:
     compress_layers: Optional[int] = field(
         default=None, metadata={"help": "The compress layers of lightweight reranker.", "nargs": "+"}
     )
+
+    def __post_init__(self):
+        # replace "\\n" with "\n"
+        if "\\n" in self.query_instruction_format_for_retrieval:
+            self.query_instruction_format_for_retrieval = self.query_instruction_format_for_retrieval.replace("\\n", "\n")
+        if "\\n" in self.examples_instruction_format:
+            self.examples_instruction_format = self.examples_instruction_format.replace("\\n", "\n")
+        if "\\n" in self.query_instruction_format_for_rerank:
+            self.query_instruction_format_for_rerank = self.query_instruction_format_for_rerank.replace("\\n", "\n")
+        if "\\n" in self.passage_instruction_format_for_rerank:
+            self.passage_instruction_format_for_rerank = self.passage_instruction_format_for_rerank.replace("\\n", "\n")

FlagEmbedding/abc/finetune/embedder/AbsArguments.py

@@ -114,6 +114,13 @@ class AbsEmbedderDataArguments:
     )
 
     def __post_init__(self):
+        # replace "\\n" with "\n"
+        if "\\n" in self.query_instruction_format:
+            self.query_instruction_format = self.query_instruction_format.replace("\\n", "\n")
+        if "\\n" in self.passage_instruction_format:
+            self.passage_instruction_format = self.passage_instruction_format.replace("\\n", "\n")
+        
+        # check the existence of train data
         for train_dir in self.train_data:
             if not os.path.exists(train_dir):
                 raise FileNotFoundError(f"cannot find file: {train_dir}, please set a true path")

FlagEmbedding/abc/finetune/reranker/AbsArguments.py

@@ -119,10 +119,17 @@ class AbsRerankerDataArguments:
         default='\n', metadata={"help": "The sep token for LLM reranker to discriminate between query and passage"}
     )
 
-    # def __post_init__(self):
+    def __post_init__(self):
-    #     for train_dir in self.train_data:
+        # replace "\\n" with "\n"
-    #         if not os.path.exists(train_dir):
+        if "\\n" in self.query_instruction_format:
-    #             raise FileNotFoundError(f"cannot find file: {train_dir}, please set a true path")
+            self.query_instruction_format = self.query_instruction_format.replace("\\n", "\n")
+        if "\\n" in self.passage_instruction_format:
+            self.passage_instruction_format = self.passage_instruction_format.replace("\\n", "\n")
+        
+        # check the existence of train data
+        for train_dir in self.train_data:
+            if not os.path.exists(train_dir):
+                raise FileNotFoundError(f"cannot find file: {train_dir}, please set a true path")
 
 
 @dataclass

FlagEmbedding/abc/inference/AbsEmbedder.py

@@ -59,7 +59,6 @@ class AbsEmbedder(ABC):
         convert_to_numpy: bool = True,
         **kwargs: Any,
     ):
-        query_instruction_format = query_instruction_format.replace('\\n', '\n')
         self.model_name_or_path = model_name_or_path
         self.normalize_embeddings = normalize_embeddings
         self.use_fp16 = use_fp16
@@ -152,6 +151,8 @@ class AbsEmbedder(ABC):
         Returns:
             str: The complete sentence with instruction
         """
+        if "\\n" in instruction_format:
+            instruction_format = instruction_format.replace("\\n", "\n")
         return instruction_format.format(instruction, sentence)
 
     def encode_queries(

FlagEmbedding/abc/inference/AbsReranker.py

@@ -149,6 +149,8 @@ class AbsReranker(ABC):
         Returns:
             str: The complete sentence with instruction
         """
+        if "\\n" in instruction_format:
+            instruction_format = instruction_format.replace("\\n", "\n")
         return instruction_format.format(instruction, sentence)
     
     def get_detailed_inputs(self, sentence_pairs: Union[str, List[str]]):

FlagEmbedding/evaluation/air_bench/arguments.py

@@ -102,3 +102,14 @@ class AIRBenchEvalModelArgs:
     compress_layers: Optional[int] = field(
         default=None, metadata={"help": "The compress layers of lightweight reranker.", "nargs": "+"}
     )
+
+    def __post_init__(self):
+        # replace "\\n" with "\n"
+        if "\\n" in self.query_instruction_format_for_retrieval:
+            self.query_instruction_format_for_retrieval = self.query_instruction_format_for_retrieval.replace("\\n", "\n")
+        if "\\n" in self.examples_instruction_format:
+            self.examples_instruction_format = self.examples_instruction_format.replace("\\n", "\n")
+        if "\\n" in self.query_instruction_format_for_rerank:
+            self.query_instruction_format_for_rerank = self.query_instruction_format_for_rerank.replace("\\n", "\n")
+        if "\\n" in self.passage_instruction_format_for_rerank:
+            self.passage_instruction_format_for_rerank = self.passage_instruction_format_for_rerank.replace("\\n", "\n")

FlagEmbedding/inference/embedder/decoder_only/icl.py

@@ -172,6 +172,8 @@ class ICLLLMEmbedder(AbsEmbedder):
         Returns:
             str: The complete example following the given format.
         """
+        if "\\n" in instruction_format:
+            instruction_format = instruction_format.replace("\\n", "\n")
         return instruction_format.format(instruction, query, response)
 
     def stop_self_query_pool(self):

scripts/hn_mine.py

@@ -90,6 +90,13 @@ class ModelArgs:
     embedder_passage_max_length: int = field(
         default=512, metadata={"help": "Max length for passage."}
     )
+    
+    def __post_init__(self):
+        # replace "\\n" with "\n"
+        if "\\n" in self.query_instruction_format_for_retrieval:
+            self.query_instruction_format_for_retrieval = self.query_instruction_format_for_retrieval.replace("\\n", "\n")
+        if "\\n" in self.examples_instruction_format:
+            self.examples_instruction_format = self.examples_instruction_format.replace("\\n", "\n")
 
 
 def create_index(embeddings: np.ndarray, use_gpu: bool = False):